license: mit
datasets:
- name: cloud_sky_vis
task:
type: image-classification
files:
- name: image_metadata.csv
format: csv
split: train
features:
- name: index
dtype: int32
- name: filename
dtype: string
- name: label
dtype: string
- name: transform_shape
dtype: string
- name: transform_min
dtype: float32
- name: transform_max
dtype: float32
Cloud and Sky Image Tensors for Classification
This dataset is designed for those interested in cloud classification projects. Due to licensing restrictions, the raw images cannot be shared publicly. However, the transformed tensors provided here are optimized for image classification tasks and are typically all you need for such projects.
Tensor Specifications
These tensors are preprocessed and normalized for use with ResNet models. The normalization parameters are as follows:
- Mean:
[0.485, 0.456, 0.406]
- Standard Deviation:
[0.229, 0.224, 0.225]
Cloud Class Labels
The dataset uses standard World Meteorological Organization (WMO) cloud classification labels, with the addition of "Clr" for clear skies. The labels used are:
As
: AltostratusCb
: CumulonimbusCc
: CirrocumulusCi
: CirrusCs
: CirrostratusCt
: ContrailsCu
: CumulusNs
: NimbostratusSc
: StratocumulusSt
: StratusAc
: AltocumulusClr
: Clear Sky
For more information on cloud classification, please refer to the WMO Cloud Atlas: WMO Cloud Classification.
Dataset Structure
- train_images_tensor.pt: Tensor of transformed images.
- train_labels_tensor.pt: Corresponding labels for the images.
- image_metadata.csv: Metadata detailing each image's properties.
Usage
These tensors are ready for direct use in training or testing ResNet and similar models for cloud classification tasks. I hope this dataset supports your research and projects.
To load the dataset:
import torch
import requests
from io import BytesIO
base_url = "https://huggingface.co/datasets/jcamier/cloud_sky_vis/resolve/main/"
tensor_url = f"{base_url}train_images_tensor.pt"
label_url = f"{base_url}train_labels_tensor.pt"
# Load the tensors
tensors = torch.load(BytesIO(requests.get(tensor_url).content))
labels = torch.load(BytesIO(requests.get(label_url).content))
# Verify the data is loaded correctly
print(tensors.shape)
print(labels.shape)
Enjoy!